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This work presents the design of a VSM based DSTATCOM with optimization of controller parameters using modified Krill Herd optimization technique with a special focus on micro grid applications. The high penetration of renewable sources into the power grid with their intermittent nature has become a great challenge for the PLL based conventional d-q frame DSTATCOM controllers to track the grid frequency...
To maintain energy balance and voltage stability is the basic function for normal operation of DC grid. Analysis and comparison of several commonly used multi-point voltage control methods, a cooperative control strategy is proposed based on improved particle swarm optimization, considering economic performance and voltage quality in both. The method established on traditional multi-point voltage...
The main objective of safe and economical operation of electric power systems is to arrange the generator set to put into operation and finally minimize the total cost while meeting certain load demand. The objective of this paper is to minimize the sum of the cost of power generation and the cost of start-up. The primal dual interior point method (PD-IPM) can effectively solve the continuous optimization...
The extreme learning machine (ELM) possesses the advantageous features of the fast learning speed, great generalization performance and high precision. However, the randomness of the parameters will influence its generalization performance and precision greatly. This paper proposes a learning algorithm which is based on the differential evolution extreme learning machine (DE-ELM) for parameter optimization...
We demonstrate the systematic implementation of recently-developed fast explicit kinetic integration algorithms that solve efficiently N coupled ordinary differential equations (subject to initial conditions) on modern GPUs. We take representative test cases (Type Ia supernova explosions) and demonstrate two or more orders of magnitude increase in efficiency for solving such systems (of realistic...
Knowledge and experience of a case manager re- mains a key success factor for Case Management Processes (CMPs). When a number of influential parameters is high, a number of possible scenarios grows significantly. Automated guidance in scenario evaluation and activity planning would be of a great help. In our previous work, we defined the statecharts semantics for visualisation and simulation of CMP...
In this investigation different algorithms for InterCriteria relations calculation are proposed. The algorithms are investigated by exploring the influence of genetic parameters on algorithm performance during the model parameter identification of E. coli fermentation process. Four different algorithms performing InterCriteria Analysis (ICrA), namely μ-biased, balanced, ν-biased and unbiased, are...
This work is concerned with the implementation of an Adaptive Fault Diagnoser (AFD) for a system modeled by Timed Continuous Petri Nets under infinite server semantics, where the set of potential faults is a priori known, however their presence during system evolution, type, location, occurrence time, magnitude and behavior over time are unknown. There exist previous works reported in literature,...
Recent innovations introduced in the electric power system, especially the significant usage of unpredictable renewable energy sources, are making to perform the needed matching of generated and absorbed electricity more difficult than in the past. To facilitate this matching, market regulation can provide economical drivers that stimulate cost-effective energy shifting by the so-called “prosumers”...
In the past few decades, analytical models for maintenance optimization have been extensively investigated. As research continues, the cases considered during the mathematical modeling are much more practical, as well as complex, which brings many difficulties for solution. In this paper, we present an exact method for solving the selective maintenance model considering multiple maintenance actions...
This paper presents a new maximum power extraction algorithm for a photovoltaic (PV) system under any variation in insolation. The new algorithm has the capability to reach global peak (GP) under any weather conditions. The main advantage of the proposed algorithm is the substitution of PI controller with direct duty cycle control scheme. To confirm the efficacy of the GWO based MPPT, it has been...
Markov Random Field (MRF) algorithms are powerful tools in image analysis to explore contextual information of data. However, the application of these methods to large data means that alternative approaches must be found to circumvent the NP-hard complexity of the MRF optimization. We introduce a MRF-based framework that overcomes this issue by using graph partitioning. The computational complexity...
Adaptive filters that employ sparse constraints or maximum correntropy criterion (MCC) have been derived from stochastic gradient techniques. This paper provides a deterministic optimization framework which unifies the derivation of such algorithms. The proposed framework has also the ability of providing geometric insights about the adaptive filter updating. New algorithms that exploit both impulse...
Blockmodelling is a technique whose aim is to identify meaningful structure in networks. Community finding is a type of blockmodelling in so far as it focuses on identifying dense subgraph structure. Generalised blockmodelling allows an analyst to explicitly control the type of extracted structure. When compared to the well studied community-finding problem, generalised blockmodelling algorithms lag...
Inversion of geophysical logging data is one of the most important tasks in oil and gas exploration. Ambiguity is usually inherent for the solutions, especially for formation with complex lithology. The optimum log interpretation technique can effectively reduce the ambiguity of the interpretation results. Therefore, the Glowworm Swarm Optimization (GSO), one of the swarm intelligence optimization...
In the present world, it is hard to overlook — the omnipresence of ‘network’. Be it the study of internet structure, mobile network, protein interactions or social networks, they all religiously emphasizes on network and graph studies. Social network analysis is an emerging field including community detection as its key task. A community in a network, depicts group of nodes in which density of links...
Literature evidences have demonstrated the effectiveness of the sampled t-way test suite for defect detection in software testing. The main task in implementing t-way testing strategies is constructing best possibility test case. There are several methods that have been proposed but none of them can be claimed to be the best result because t-way are considered as NP-hard problem. In this paper, the...
Due to the constant growth of the embedded systems complexity and the increasing number of mobile devices, there is a increasing demand for low power multiprocessor platforms. It is known that the cache memory contributes with a representative percentage of energy consumption of a MPSoC processor, so that it is very important to use an optimal cache configuration for an embedded application in order...
This work presents cost-effective low-rank techniques for designing robust adaptive beamforming (RAB) algorithms. At first, we introduce an orthogonal Krylov subspace projection mismatch estimation (OKSPME) method, in which a general linear equation is considered in large dimensions which aims to solve for the steering vector mismatch with known information, then we employ the idea of the full orthogonalization...
Over the past several decades, function optimization has been a growing topic in the field of computational intelligence. This is partly down to the myriad of real world problems that function optimization can be applied to, but also the fact there are a number of issues facing optimization algorithms that are still yet to be solved. Such problems include getting stuck in local optima, and balancing...
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